Nutritional Considerations for Sport Participation in Children and Adolescents With Obesity
Bibliographic record
Abstract
There is a high prevalence of children with obesity who are participating in sports. Appropriate nutritional considerations are important to optimize health and training adaptations. This review focuses on macronutrient recommendations and their effect on weight management and/or benefits for athletic training for children ages 5 to 18 years. Equal distribution of protein intake throughout the day (~25-30 g/meal) and during postexercise recovery is recommended. Special attention should be given to increasing protein intake during breakfast because it is often the meal with the least protein intake. Both postexercise recommendations for protein (~0.3 g/kg of body weight) and carbohydrate (~1.0-1.2 g/kg/h) were not determined in children with obesity, and require future verification. Individual carbohydrate needs of training to meet fuel costs are recommended, but ~200 to 500 g/day of carbohydrate may be required depending on a child's level of sport participation and competition. Fat intake should follow general recommendations to meet the accepted macronutrient distribution range in children (25% to 35%) and reduce saturated fat intake. No evidence suggests that additional dietary fat modifications would improve training adaptations in children. Longitudinal studies are required to further our understanding of age and sex effects and confirm the appropriate quantity of macronutrients for active children with obesity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".